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<h4><a name="Datasets" id="Datasets"></a>Known data-sets</h4>
<p>
SocNetV can recreate easily one of the following known data-sets:

<ul>
            <li>Krackhardt: High-tech managers (advice), 24 actors</li>
            <li>Krackhardt: High-tech managers (friendship), 24 actors</li>
            <li>Krackhardt: High-tech managers (Reports To), 24 actors</li>
            <li>Padgett: Florentine Families (marital relationship), 16 actors</li>
            <li>Padgett: Florentine Families (business relationship), 16 actors</li>
            <li>Zachary: Karate Club (simple ties), 34 actors</li>
            <li>Zachary: Karate Club (weighted ties), 34 actors</li>
            <li>Bernard: Killworth Fraternity (multirelational), 58 actors</li>
            <li>Galaskiewicz: CEOs and clubs (affiliation data)</li>
            <li>Freeman's EIES networks (multirelational, 32 actors)</li>
            <li>Freeman: EIES network, at time-1, 48 actors</li>
            <li>Freeman: EIES network, at time-2, 48 actors</li>
            <li>Freeman: EIES network, number of messages, 48 actors</li>
            <li>Freeman: The 34 possible graphs with N=5 (as multirelational), 5 actors</li>
            <li>Mexican Power Network in the 1940s (list format)</li>
            <li>Knocke: Bureacracies Information Exchange Network, 10 actors</li>
            <li>Stephenson and Zelen (1989): Network of 40 AIDS patiens (sex relationship)</li>
            <li>Stephenson and Zelen (1989): Information Centrality test dataset, 5 actors</li>
            <li>Wasserman and Faust: star, circle and line graphs of 7 actors (multirelational)</li>
            <li>Wasserman and Faust: Countries Trade (basic manufactured goods), 24 actors</li>
            </ul>
</p>

<p>
From File menu select "Create known data set" or press F7.
A dialog will appear where you select one of the data-sets above.
Press OK and the network will be displayed in the canvas.
<p>


<h4><a name="Random" id="Random"></a>Random network creation</h4>
<p>
SocNetV can create a random network for you. At the moment, it can create the following types of random networks:
</p>
<ul>
<li><b>Scale-free</b></li>
<li><b>Small world</b></li>
<li><b>Erdos-Renyi</b></li>
<li><b>Ring lattices</b></li>
<li><b>d-regular</b></li>
</ul>

<h5><a name="RandomSF" id="RandomSF"></a>Scale-free</h5>
<p>SocNetV generates random scale-free networks of <span style=" font-style:italic;">n</span>
nodes according to the Barabási–Albert (BA) model which uses a preferential attachment mechanism.

<p>The algorithm starts with the given \( m_0 \) connected nodes.

In each step a single new node is added, along with \( m \) edges to existing nodes.</p>

<p>The probability that the new node will connect to an existing node \( i \)
 is:
\( p_i = \frac { (α + d_i ^ p) } {  \sum_j {d_j} } \)

where: <br />
\( α \) the  initial 'attractiveness' of each node,<br />
\( d_i \) the degree of node \( i \)<br />
\( \sum_j {d_j}  \)  the sum of degrees of all pre-existing nodes \( j  \)<br />

</p>
<p> if \( α = 0 \) and \( p = 1 \) then the preferential attachment is linear (BA model).
</p>

<h5><a name="RandomSW" id="RandomSW"></a>Small Worlds</h5>
<p>SocNetV creates small worlds using the Watts and Strogatz model.
According to that model, a 'small world' is a random network with short average path lengths and high clustering.
<br />
Given the desired number of nodes N, the mean degree K (assumed to be an even integer),
and a special parameter \(  \beta \), satisfying \(  0 \le \beta \le 1 \)
and \(  N\gg K \gg \ln(N)\gg 1 \), the model constructs an undirected graph with N nodes and
\(  \frac{NK}{2} \) edges in the following way:</p>
<ul>
<li>Construct a regular ring lattice, a graph with \(  N \) nodes each connected to \(  K \) neighbors, \(  K/2 \) on each side.
</li><li>
For every node \( n_i=n_0,\dots, n_{N-1} \) take every edge \( (n_i, n_j) \) with \( i < j \), and rewire it with probability \( \beta \). Rewiring is done by replacing \( (n_i, n_j) \) with \( (n_i, n_k) \) where \( k \) is chosen with uniform probability from all possible values that avoid self-loops (\( k \ne i \)) and link duplication (there is no edge \( (n_i, n_{k'}) \) with \( k' = k \) at this point in the algorithm).
</li>
</ul>

<p>From the menu Network select Create Random Network > Small World (or press Shift+W).
<br />
You will be asked for the number of nodes N, their mean degree K and a rewiring probability \(  \beta \). </p>


<h5><a name="RandomER" id="RandomER"></a>Erdos-Renyi networks</h5>
<p>According to G(n, p) model (Erdos-Renyi), a random network is created by connecting nodes randomly. </p>
<p>Each edge is included in the graph with equal probability P, independently of the other edges.</p>
<p>From the menu Network select Create Random Network > Erdos-Renyi (or press Shift+R). </p>
<p>You will be asked for the number of nodes and an edge probability. </p>

<h5><a name="RandomER" id="RandomER"></a>Ring lattices</h5>

<p>Ring lattices (or physicist's lattices) are 'random' networks where all nodes are positioned in a ring.</p>
<p>Each one has the same even degree (number of edges) d with her "neighbourhood", namely she is linked with the d/2 nodes before and d/2 nodes after her.
<p>For instance in a 4-lattice of 10 nodes, node 6 will be linked with 4, 5, 7 and 8. </p>
<p>To create a ring lattice network click Network > Create Random Network > Ring Lattice (or press Shift+L). </p>
<p>You will be asked for the number of nodes and the degree of each node.</p>

<h5><a name="RandomER" id="RandomRegular"></a>d-regular networks</h5>
<p>These are random network where each node have the same number of "neighbours", aka the same degree d.
<p>Nodes are arbitrarily linked with each other other. </p>





<h4><a name="WebCrawler" id="WebCrawler"></a>Web Crawler</h4>
<p>
SocNetV includes a simple web crawler, which consists of two parts:
a spider and a parser.</p>
<p>
The spider visits a given initial URL (i.e. a website or a webpage) and downloads its HTML code. <br />
The parser scans the code for 'href' links to other pages (internal or external)
and adds them to a queue of URLs (called frontier).<br />
As URLs are added in the queue, the spider visits them and downloads their HTML
which is scanned for more links by the parser, and so on...<br />
The end result is the 'network' of all visited webpages as nodes and their real links as edges.
<p>
Please note that the parser searches for 'href' links only in the body section of the HTML code.
</p>

<p>
To start the web crawler, go to menu Network > Web Crawler or press Shift+C.
A dialog will appear, where you must enter the initial web page (seed).
</p>

<p>
You can also set the maximum nodes/pages (default 600) and what kind of links to
crawl: internal, external or both. <br />
By default the spider will crawl both internal and external links.
</p>

